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We are seeking a highly impactful individual who excels in individual contributions but also significantly elevates the team's overall performance. An Engineer with a can-do attitude who has what it takes to ship state of the art applications using cutting-edge technologies.
As an iOS Engineer, youll be joining a diverse team of professionals with some of the brightest, sharpest, and analytical minds.
You will have a huge impact on our products, taking full ownership of features E2E, and having a great influence inside the team, on both the rider and driver apps.
What you can expect from us:
You'll work with the latest technologies, including TCA, Swift UI, Swift Concurrency, and more
Work with a product team that maintains high UX/UI standards and a robust design system, making it easy to build elegant, consistent, and user-friendly features
Our apps heavily utilize RxSwift, and our team is at the forefront of RxSwift and Functional Reactive Programming (FRP)
We maintain very high standards of quality and stability, with our crash-free session rate typically at an impressive 99.98%
We automate a lot of tedious tasks like signing, releasing, localizing, and testing, freeing you to focus on developing awesome features
We foster a culture where self-initiative is encouraged, and developers are empowered to push their own ideas, not just work on planned tasks.

It starts with you - a technical leader whos passionate about building resilient, automated infrastructure and growing high-performing teams. You care about operational excellence, developer experience, and enabling AI-driven teams to move fast with confidence. Youll lead the DevOps team in architecting and operating the compute and networking infrastructure that powers our AI platform - from CI/CD pipelines to Kubernetes clusters to observability systems.
If you want to lead a team that builds the infrastructure foundation for mission-critical AI systems, join mission - this role is for you.
:Responsibilities
Lead and grow the DevOps team - hiring, mentoring, and developing engineers while fostering a culture of ownership and continuous improvement.
Define compute and networking infrastructure strategy across cloud and on-prem environments; drive architectural decisions that balance reliability, security, cost, and velocity.
Own the platforms deployment, scaling, and operational posture - ensuring systems meet demanding SLAs for government and national-scale customers.
Build and evolve CI/CD pipelines for application and service deployments with automated testing, security scanning, and rollback capabilities.
Drive infrastructure-as-code practices for compute, networking, and orchestration - ensuring reproducible, auditable, and version-controlled infrastructure across all environments.
Enable AI-native operations - support agentic deployment pipelines, self-healing infrastructure, and secure sandboxing for model experimentation.
Establish observability, alerting, and incident response practices that provide visibility into system health and enable fast recovery.
Partner with Engineering, Data Platform, Data Engineering, and Security teams to align infrastructure capabilities with platform needs.
Establish infrastructure characteristics (availability, latency, throughput) that enable data freshness, correctness, and low-latency pathways for AI training/inference, retrieval, and agentic workflows.
Ship paved-road developer tooling - shared templates, CI/CD workflows for services, IaC modules for compute and networking, and runbooks - to standardize best practices across engineering teams.
Collaborate with Engineering, Data Platform, Data Engineering, Security, Product, AI/ML, Data Science, and Analytics to align infrastructure capabilities with evolving platform and data product needs.

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI infrastructure. This group builds scalable, high-performance AI systems for internal users and external customers, designed to run seamlessly across cloud and on-premise environments using the latest hardware advancements.
:Responsibilities
Design and optimize LLM serving infrastructure using inference engines (vLLM, TensorRT-LLM, Triton Inference Server)
Implement and tune distributed inference strategies including tensor parallelism, pipeline parallelism, and multi-node serving
Develop and apply model compression techniques to optimize cost, latency, and memory footprint while maintaining model quality
Build self-service fine-tuning platforms that enable data scientists to run experiments (LoRA, QLoRA, full fine-tuning) in a standardized, reproducible, and governed manner
Optimize inference performance through batching strategies, KV-cache tuning, and speculative decoding
Develop reusable APIs, abstractions, and platform services for model deployment, scaling, and lifecycle management
Collaborate with AI researchers and product teams to productionize models and meet latency/throughput requirements
Evaluate and benchmark new model architectures, compression methods, and serving frameworks

Were looking for a passionate, creative, and highly motivated Product Designer to join our Mobile and Web Product teams. The team is responsible for all aspects of product development for the Rider, Driver Apps, and Business platform, used by millions of users globally. As a product designer, you will be involved in all the development aspects of our wide range of products and features: from the initial research, user-journey design, wireframing, and prototyping to the launch and beyond. Youll be collaborating with other product designers, product managers, and engineers.
We expect you to be:
Owner of the product's overall look and feel.
Product thinker - identifying and understanding users needs and new opportunities derived from them.
Problem solver - taking complex problems and coming up with well-researched and elegant solutions based on the use of UX methodology.
Data-driven designer - making design decisions based on data, user behavior, and measurable outcomes to ensure impact and continuous improvement.
Pixel-perfect freak - compromising on the smallest details.
Team player - demonstrating great interpersonal skills while interacting with the product managers R&D teams and other stakeholders.
Fast learner - evolving fast, while being capable of learning from both success and failure.

It starts with you - ML Engineering Team Lead responsible for leading a team delivering end-to-end ML systems. This role combines people management with ownership of model training, evaluation, deployment, and ML platform standards
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you. :
: Responsibilities
Lead and mentor ML Engineers delivering production ML systems.
Own technical direction for model architectures, training pipelines, and evals.
Review designs and code; ensure scalability, reliability, and data quality.
Define standards for experimentation, reproducibility, and monitoring.
Partner with product and platform teams on roadmap and prioritization.
Balance hands-on technical contribution with people management.
Build a culture of measurement, rigor, and continuous improvement.

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI infrastructure. This group builds scalable, high-performance AI systems for internal users and external customers, designed to run seamlessly across cloud and on-premise environments using the latest hardware advancements.
:Responsibilities
Design, build, and maintain scalable Kubernetes-based infrastructure for ML workloads across on-premise and cloud environments
Architect hybrid infrastructure solutions enabling seamless model flow from on-premise training environments to cloud-based inference deployments
Implement model registry and artifact management strategies that support cross-environment synchronization, versioning, and governance
Design secure, efficient data and model transfer mechanisms between on-premise and cloud (networking, storage replication, caching strategies)
Implement and manage GPU scheduling, resource allocation, and cluster autoscaling for heterogeneous compute environments
Build and maintain CI/CD pipelines for ML systems, including model versioning, testing, and promotion across environments
Develop observability solutions (logging, monitoring, alerting) for ML infrastructure across hybrid deployments
Collaborate with ML Engineers to define infrastructure requirements and SLAs for training and serving workloads

Were looking for an experienced AI Project Manager to lead and coordinate cross-functional projects within our AI Group. You will partner with research, data science, and product leaders to translate strategy into action, managing timelines, dependencies, and priorities for our most advanced AI initiatives.
:Responsibilities
Lead the planning, execution, and delivery of multiple AI projects across research and production teams.
Partner with AI scientists, ML engineers, and product stakeholders to define goals, success metrics, and execution plans.
Establish and maintain clear project roadmaps, ensuring alignment with the AI Groups strategic priorities.
Manage communication across technical and non-technical stakeholders, providing visibility into progress and blockers.
Drive agile processes within AI teams to support experimentation, iteration, and scalable delivery.
Identify and mitigate project risks, ensuring smooth collaboration across research, data, and engineering functions.
Promote a culture of technical rigor, transparency, and innovation within the AI Group.

It starts with you - a senior AI engineer focused on building production-grade agentic systems: autonomous agents, MCP tools, agentic workflows, and rigorous evaluation.
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Design and build single- and multi-agent systems with planning, memory, and tool use.
Implement and operate MCP tools and servers with secure schemas and permissions.
Build agentic workflows using LangChain, LangGraph, or equivalent frameworks.
Integrate LLMs via SDKs; manage prompts, structured outputs, and tool calling.
Define and run LLM evals (quality, correctness, latency, cost, regressions).
Add observability and reliability (logging, tracing, retries, state management).
Optimize performance and cost; take systems to production.
Mentor engineers and establish agentic best practices.

It starts with you - an engineer driven to build modern, real-time data platforms that help teams move faster with trust. You care about great service, performance, and cost. Youll architect and ship a top-of-the-line open streaming data lake/lakehouse and data stack, turning massive threat signals into intuitive, self-serve data and fast retrieval for humans and AI agents - powering a unified foundation for AI-driven mission-critical workflows across cloud and on-prem.
If you want to make a meaningful impact, join mission and build best-in-class data systems that move the world forward - this role is for you.
:Responsibilities
Build self-serve platform surfaces (APIs, specs, CLI/UI) for streaming and batch pipelines with correctness, safe replay/backfills, and CDC.
Run the open data lake/lakehouse across cloud and on-prem; enable schema evolution and time travel; tune partitioning and compaction to balance latency, freshness, and cost.
Provide serving and storage across real-time OLAP, OLTP, document engines, and vector databases.
Own the data layer for AI - trusted datasets for training and inference, feature and embedding storage, RAG-ready collections, and foundational building blocks that accelerate AI development across the organization.
Enable AI-native capabilities - support agentic pipelines, self-tuning processes, and secure sandboxing for model experimentation and deployment.
Make catalog, lineage, observability, and governance first-class - with clear ownership, freshness SLAs, and access controls.
Improve performance and cost by tuning runtimes and I/O, profiling bottlenecks, planning capacity, and keeping spend predictable.
Ship paved-road tooling - shared libraries, templates, CI/CD, IaC, and runbooks - while collaborating across AI, ML, Data Science, Engineering, Product, and DevOps. Own architecture, documentation, and operations end-to-end.

we are looking for a Senior Data Scientist - AI Research Group.
:Responsibilities
Conduct hands-on research and development of state-of-the-art models, algorithms, and systems.
Lead research projects end-to-end from problem formulation, ideation, and experimental design to prototyping and transition into production.
Explore new methodologies and develop creative approaches to solve complex challenges.
Identify ambitious research directions and invent novel approaches to solve complex, high-impact problems.
Shape the research roadmap and influence long-term technical strategy across the organization.

It starts with you - an engineer driven to operate database systems at the highest level of reliability and performance. You care about query latency, uptime, data durability, and getting paged as little as possible. Youll operate, tune, and scale the database engines that serve the platform - from PostgreSQL to Elasticsearch, Redis to vector databases, across cloud and on-prem environments.
If you want to run the databases that power mission-critical AI at national scale, join Dreams mission - this role is for you.
The Dream-Maker Responsibilities
Operate and maintain database systems - PostgreSQL, Elasticsearch, Redis, MongoDB, vector databases, and others across cloud and on-prem.
Own database reliability - high availability configurations, replication, failover automation, and SLA adherence.
Drive performance tuning - query optimization, index design, configuration tuning, and resource profiling to meet latency and throughput targets.
Execute operational procedures - backup/recovery, disaster recovery testing, upgrades, migrations, and capacity scaling.
Lead incident response for database issues - troubleshooting production problems, root cause analysis, and implementing preventive measures.
Build monitoring and alerting - dashboards, metrics collection, slow query analysis, and proactive capacity alerts.
Enable new capabilities - deploying and tuning vector databases for AI workloads, evaluating new database technologies.
Collaborate with Data Platform, Data Engineering, Engineering, and Security teams on database operations and best practices.
Uphold database SLAs that support retrieval paths, feature stores, and embedding durability; coordinate safe schema evolution, partitioning, and replay/backfill practices.
Expose catalog and lineage signals - ownership, change history, and impact analysis - to improve trust and safe consumption for downstream users.
Collaborate with Data Platform, Data Engineering, Engineering, Security, Product, AI/ML, Data Science, and Analytics to balance performance, durability, and evolution across workloads.

It starts with you - an engineer driven to build resilient, automated infrastructure that enables teams to move fast with confidence. You care about operational excellence, developer experience, and reliability at scale. Youll architect and operate the compute and networking infrastructure that powers our AI platform - from CI/CD pipelines to Kubernetes clusters to observability systems - across cloud and on-prem environments.
If you want to build infrastructure that powers mission-critical AI systems at national scale, join mission - this role is for you.
:Responsibilities
Architect and operate Kubernetes-based infrastructure across AWS and on-prem environments, ensuring high availability, security, and performance.
Design and maintain CI/CD pipelines for application and service deployments with automated testing, security scanning, and rollback capabilities.
Drive infrastructure-as-code practices for compute and networking - building reproducible, auditable, and version-controlled infrastructure.
Own reliability and incident response - establish SLOs, build alerting systems, lead incident resolution, and drive post-incident improvements.
Enable AI-native operations - support agentic deployment pipelines, self-healing infrastructure, and secure sandboxing for model experimentation.
Build and maintain observability systems - metrics, logging, tracing, and dashboards that provide visibility into system health.
Optimize infrastructure cost and performance - right-size resources, implement auto-scaling, and identify efficiency opportunities.
Collaborate with Engineering, Data Platform, Data Engineering, and Security teams to align infrastructure with platform needs.
Shape infrastructure characteristics that support data freshness, correctness, and low-latency pathways for AI training/inference, retrieval, and agentic workflows.
Contribute paved-road tooling - reusable CI/CD patterns for services, IaC modules for compute and networking, and runbooks - that streamline delivery across teams.
Collaborate with Engineering, Data Platform, Data Engineering, Security, Product, AI/ML, Data Science, and Analytics to anticipate and meet cross-functional needs.

It starts with you - a senior ML engineer responsible for building, training, evaluating, and operating machine learning systems in production. The role focuses on data pipelines, model training, experimentation, evaluation, and scalable deployment.
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.

we are looking for a Senior NLP Researcher.
:Responsibilities
Conduct cutting-edge research in natural language processing and large language models.
Design, train, and optimize large-scale neural network models for advanced applications.
Transition research projects from ideation through deployment and scaling.
Collaborate closely with cross-functional teams, including domain experts, product managers, and engineers, to deliver impactful AI solutions.
Define and contribute to the AI and NLP product roadmap.

we are seeking a skilled and adaptable Solution Architect to lead deep technical engagements during both pre-sales and post-sales phases. You will ensure seamless deployment, resolve complex technical issues, and deliver long-term value to customers through AI-powered cybersecurity platform.
This role also includes ownership of lab environments to simulate customer use cases, validate solutions, and support internal testing and demos-making it a key technical bridge between Product, Engineering, and Customer Success.
:Responsibilities
Build and maintain strong relationships with customers, serving as a trusted technical advisor to understand and address their needs, challenges, and goals.
Collaborate with internal teams, including R&D and Product, to align customer feedback with roadmap priorities and platform improvements.
Lead the technical aspects of Proof of Concepts (PoCs) and guide customers through pre-deployment readiness.
Drive post-sales success by resolving complex issues, optimizing deployments, and identifying opportunities to improve security posture.
Deliver technical enablement and training sessions for customer teams to ensure strong adoption and platform fluency.
Stay informed on the latest in cybersecurity, infrastructure, AI, and cloud technologies to continuously elevate customer outcomes.
Lab Environment Ownership: Responsible for designing, maintaining, and automating lab environments that simulate real world customer deployments to test product features, validate integrations, demonstrate the platform, and support cross-functional testing and customer issue replication.